Analysis of accounting multipliers in a NAMEA:
An application to the case of Catalonia
Llop, Mariaa*; Pié, Laiab
a
Universitat Rovira i Virgili, Departament d’Economia, Avgda. Universitat nº 1, 43204 Reus, Spain,
Tel. +34 977 759851, Fax. +34 977 300661, [email protected]
b
Universitat Rovira i Virgili, Departament d’Economia, Avgda. Universitat nº 1, 43204 Reus, Spain,
Tel. +34 977 759884, Fax. +34 977 300661, [email protected]
*Corresponding authour
Abstract
The aim of this paper is the analysis of the Catalan economy (2001) with the
use of a National Accounting Matrix with environmental accounts (NAMEA) for the
Catalan economy with 2001 data. We will focus on the analysis of the emission
multipliers and we will also analyse the impact of a 10% reduction in greenhouse
emissions on emission multipliers. This emission-reduction percentage would bring the
Catalan economy into compliance with the maximum emissions level allowed by the
Kyoto Protocol. We consider three possible scenarios that would allow this goal to be
met. First, we will simulate a 10% reduction in regional emissions and a 5% drop in the
endogenous income of the multipliers’ model (production, factorial and private
income). Second, we will simulate a 10% reduction in emissions and a 10% increase in
endogenous income. Finally, we will simulate a 10% reduction in emissions and a 5%
increase in endogenous income. Additionally, we will analyse the decomposition of the
emission multipliers into own effects, open effects and circular effects to capture the
different channels of the emission generation process.
Keywords: NAMEA, emission multipliers, Kyoto Protocol.
Topic: 05. Sustanainable development and input-output models in environmental
economics.
2
Llop, M; Pié, L
1. Introduction
Nowadays, there is constant economic growth in the industrialized countries,
together with an increasing population in the developing countries. This can rise the
demand for resources and the negative impacts of economic activity, and is the reason
why developing countries have recently expressed concerns about obtaining higher and
more equitable economic growth whilst at the same time reducing the associated
environmental damage.
There has been much debate in recent times regarding the relation between
economic activity and the environment and the measures that need to be taken to
preserve the natural habitats. In fact, the environmental deterioration has recently
focused the attention of both economists and ecologist that have integrated ideas and
concepts. On the one hand economic activities make use of natural resources, and on the
other they generate emissions. However, the national accounts systems do not take into
account that the environmental data may be related with the mechanisms that determine
the circular flow of income. Some experts have proposed the creation of an integrated
system of environmental-economic accounting that allows the evaluation of policies
designed to attain sustainable development. There are also authors that argue that
environmental degradation should appear as a discount factor in the national accounts
system. This would permit countries’ economic growth to be accurately estimated.
In 1993, the United Nations published the System of National Accounts
(United Nations, 1993) in which it was formulated, for the first time, an accounting
framework for assessing national accounts and environmental statistics. Afterwards,
this integrated system was revised and published in a handbook (United Nations,
2003) that permits a consistent analysis of the environment’s contribution to the
economy and the economy’s impact on the environment.
In addition to the efforts by the United Nations to integrate economic and
environmental accounts, studies on incorporating environmental impacts in the social
accounting matrix (SAM) framework emerged in the 1990s. For example, Keuning
(1992, 1993 and 1994) proposed the development of a national accounting matrix that
would include environmental accounts. In this matrix, the economic variables would be
expressed in monetary terms and the environmental ones in physical terms.
Subsequently, Xie (1995) constructed an environmental SAM for China that took into
consideration polluting emissions.
De Haan and Keuning (1996) presented a National Accounting Matrix
including Environmental Accounts (NAMEA) for the Netherlands. Keuning, Dalen and
De Haan (1999) described an aggregated NAMEA which they used to compare the
contribution of economic activities to economic indicators with the contribution of
economic activities to environmental themes. They also described how economic
activities contribute cumulatively to economic and environmental indicators (thus
taking into account the relations between the production activities) and described a
number of recent applications and extensions of the NAMEA in the Netherlands.
Ike (1999) described a NAMEA for Japan which provided a comprehensive
and consistent picture of the interrelationship between the economy and the natural
environment, a basis on which cost-benefit analysis could be applied and the necessary
information for policy planning. This NAMEA showed environmental pressures not
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Analysis of accounting multipliers in a NAMEA:
An application to the case of Catalonia
3
only from domestic pollutant emissions, but also from transboundary flows from the
rest of East Asia.
Vaze (1999) described how environmental accounts carried out in the UK were
calculated. Results from the pilot accounts were reproduced in a NAMEA framework,
which allowed the comparisons with the NAMEAs calculated by other countries. Xie
and Saltzman (2000) constructed a numerical version of the environmentally extended
SAM using Chinese data from 1990. Multiplier and structural-path analyses were
applied to this database to assess the environmental impacts of pollution-related
economic policies. Xie (2000) then extended the SAM to capture the relationships
among economic activities, pollution abatement activities, and pollution emissions. The
author presented a numerical example of the environmentally extended social
accounting matrix (ESAM) using Chinese data from 1990. Multiplier and structural
path analyses were applied to the ESAM to assess the environmental impacts of
pollution-related economic policies. The results showed that an integrated economicecologic database can be a useful tool for environmental policy analysis.
De Hann and Keuning (2001) showed how environmental issues can be
incorporated into macroeconomic accounting trough the construction of a National
Accounting Matrix including Environmental Accounts (NAMEA) for the Netherlands.
The paper discussed a number of conceptual issues on the harmonisation of
environmental statistics and national accounts. Specific attention was given to
consistently accounting for the pollution generated by production and consumption
activities and to the importance of aggregated environmental indicators.
For Spanish applications, Manresa and Sancho (2004) conducted the first
integrated economic and environmental analysis for Catalonia, taking 1987 as the base
year. The paper analysed the sectorial power intensity of the Catalan economy using a
regional SAM that differentiated between the polluting emissions originating from
production and those originating from final consumption. The authors observed that the
energy sectors themselves were the largest consumers of energy sources. Rodríguez,
Llanes and Cardenete (2007) showed that a SAM including environmental accounts can
be used for economic and environmental efficiency analysis. They used Spanish data for
the year 2000 and applied it to water resources and greenhouse gas emissions. Finally,
Flores and Sánchez (2007) analysed and the most important environmental impacts on
the economy of Aragon by relating the main economic activities to resource
consumption and pollution levels. To carry out this analysis, they constructed a Social
Accounting Matrix including Environmental Accounts (SAMEA) for Aragon for 1999.
The methods that integrate economic information and environmental effects
are very useful for determining the environmental consequences of economic activity.
The improvement of databases and linear models will allow the effects of the circular
flow of income and the associated environmental loads to be analysed jointly. This area
of research captures both environmental and economic aspects of the environmental
problems that affect the global economy.
The aim of this paper is to analyse the Catalan economy (2001) with a linear
model. We will focus on the analysis of the classic multipliers (Stone-Pyatt and Round)
and the associated emission multipliers. To complete the analysis, we also analyse the
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Llop, M; Pié, L
impact of a 10% reduction in greenhouse emissions on emission multipliers.1 This
emission-reduction percentage would bring the Catalan economy into compliance with
the maximum emissions level allowed by the Kyoto Protocol. We consider three
possible scenarios that would allow this goal to be met. First, we simulate a 10%
reduction in regional emissions and a 5% drop in the endogenous income of the
multipliers’ model (production, factorial and private income). Second, we simulate a
10% reduction in emissions and a 10% increase in endogenous income. Finally, we
simulate a 10% reduction in emissions and a 5% increase in endogenous income. We
also analyse the decomposition of the emission multipliers into own effects, open
effects and circular effects to capture the different channels of emission generation.
If we wish to decrease the level of polluting emissions but maintain a high
standard of living in society, it is essential to analyse the various policies available in
order to understand the effects involved. Policies designed to reduce emissions may
clash with society’s development objectives, since economic growth, the consumption
of natural resources, and pollution are closely related. An attempt should be made,
therefore, to establish a link between economic activity and environmental impacts in
order to ensure sustainable development.
The rest of the paper is organised as follows. The next section describes the
linear SAM model. Section 3 presents the decomposition of the multipliers. Section 4
describes the extension of the SAM model with greenhouse emissions and Section 5
analyses the results. The paper ends with a conclusion section.
2. The Linear SAM Model
The linear SAM model shows the released effects generated in the economic
activity of the various agents with a perspective of the circular flow of income. The
relations captured by this model incorporate interdependences within the productive
sphere, final demand decisions, and income distribution operations.
SAM models calculate countable or extended multipliers that quantify the
global effects in terms of increase in income, produced by exogenous income
instruments. By analysing the extended multipliers, it is possible to determine which
agents have the greatest effects on economic activity and which the smallest. In fact, the
SAM model is similar to the input-output model, but with one clear difference: the
extended multipliers incorporate in the process of income creation not only production
relations, but also relations of income distribution and final demand.
The origins of this method are found in the pioneering works of Stone (1978),
and Pyatt and Round (1979), which used a SAM of the Sri Lankan economy to show the
relationships between production, income, and demand. Defourny and Thorbecke
(1984) proposed a complementary analysis of traditional multipliers: the structural-path
On 23 January 2008, the European Commission met to endorse an action plan to fight climate change known as “20
20 by 2020”. This has become the mantra used by the Commission to present itself to the rest of the world as a
champion in the fight against climate change. The plan calls for a 20% reduction in CO 2 emissions and the use of
20% of renewable energy sources (with 10% of fuel from biofuel) by 2020. In order to reach these percentages, each
country must contribute in proportion to its per capita GDP. Spain will have to obtain 20% of its energy from
renewable energy sources by 2020, which is more than twice the current level (8.7% in 2005), and reduce its
greenhouse gas emissions by 10%.
1
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An application to the case of Catalonia
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analysis. This contribution captured not only the influence but also the transmission
channels of the multiplier effects between the various agents in the economy.
The starting point in the SAM model is to divide accounts into two types:
endogenous and exogenous. Table 1 contains the accounting identities inherent to a
SAM in which the accounts have been divided into these two types.
[PLACE TABLE 1 HERE]
According to table 1, the sum of the row of endogenous accounts is column
vector Y with two different parts: the endogenous accounts (Tnn, whose sum is
represented by vector n) and the exogenous accounts (Tnx, whose sum is represented by
vector x). In other words:
Y  n  x.
(1)
The components of the matrix of transactions between endogenous accounts,
Tnn, can be obtained from the ratios of the corresponding totals in columns:
Tnn  AŶ ,
(2)
where Yˆ is the diagonal matrix of the elements of vector Y. Similarly, matrix A
contains the transactions of each endogenous account in relation to the column total in
T 

the SAM  aij  ij  .
Y 

Vector n can be obtained by using matrix A in the following way:
n  AY.
(3)
By combining expressions (1) and (3), we obtain:
Y  n  x  AY  X  I  A1 X  MX ,
(4)
where Y is the vector of endogenous income in every account, I is the identity matrix, A
is a matrix of structural coefficients (calculated by dividing the transactions in the SAM
by total endogenous income) and X is the vector of exogenous income. In expression
(4), M= (I − A)− 1 is the matrix of SAM multipliers. This matrix shows the overall
effects (direct and indirect) on the endogenous accounts caused by unitary and
exogenous changes in the exogenous income of accounts.
Within the structure of a SAM, the accounts that represent potential tools of
economic policy or variables determined outside the economic system are traditionally
considered exogenous. The usual assumption of endogeneity made in SAM models
follows the Pyatt and Round (1985) criteria, which consider sectors of production,
factors (labour and capital), and private consumers as endogenous components. On the
other hand, the government, the saving-investment account and the foreign sector are
considered exogenous components. This assumption, therefore, captures the complete
relationships of the circular flow of income and shows the connections between
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productive income, factorial and personal distribution of income, and consumption
patterns. The SAM model is similar to the input-output model but with one clear
difference: in the process of income creation, the extended multipliers incorporate not
only production relations but also relations of income distribution and final demand.
3. Decomposition of the multipliers
The traditional endogeneity assumption of Stone (1978) and Pyatt and Round
(1979) considers activities, factors of production and households to be endogenous
components. So, matrix A of structural coefficients has the following structure:
 A11

A   A21
0

A13 

0 ,
A33 
0
0
A32
where A11 contains the input-output coefficients, A13 contains the coefficients of the
household sectorial consumption, A21 contains the factors of production coefficients,
and A32 contains the coefficients of factor income of consumers. The SAM model
completes the circular flow by capturing not only the intermediate demand relations, but
also the relations between factor income distribution and private consumption.
To provide a deeper insight into the analysis of SAM multipliers, Pyatt and
Round (1979) divided matrix M into different circuits of interdependence. Specifically,
it can be seen that:
Y  AY  X




  A  A Y  AY  X


1




 

  I  A   A  A Y  X 

 


1




 AY   I  A  X


1






 A 2 Y   I  A  I  A  X





 



 A3 Y   I  A  A 2  I  A




 M 3M 2M1 X ,


1
X
(5)
1
1
 

 





where A   I  A  A  A , M 1   I  A  , M 2   I  A A2  , and M 3








1
 

  I  A3  .





Finally, matrix A has the following structure:
 A11 0 0 


A  0
0 0 .
0
0 A33 


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In the expression above, matrix M of total SAM multipliers has been defined
by three multiplicative components that convey different economic meanings. 2 After the
corresponding matrix algebra has been applied, it can be seen that the first block M1 has
the following elements:
I  A11 

M1   0
 0



0
.
1 
I  A33  
0
0
I
0
Matrix M1 contains the own effects explained by the connections between the
accounts belonging to the same income relationships. Specifically, the perspective of
income transmission reflected in M1 responds to the effects of intersectorial linkages
and the effects of transactions between consumers.
Additionally, matrix M2 is a follows:

I

M2  
A21

I  A33 A32 A21
I  A11 1 A13 I A32
I  A11 1 A13 

1
A21I  A11  A13 .
I
I  A33 1 A32


I
This block contains the open effects caused by the accounts on the other parts
of the circular flow of income. As it shows the effects of the accounts on the other
income circuits of the system, the main diagonal in M2 is unitary and the other elements
are positive.
Finally, matrix M3 has the following structure:


 I  I  A 1 A I  A 1 A A 1
11
13
33
32 21


M3  
0

0


I  A
21
0
I  A11 1 A13 I  A33 1 A32 
1
0



0

1 
1
1
I  I  A33  A32 A21 I  A11  A13 

0


Block M3 contains the circular effects on the accounts that are activated
because of exogenous inflows. Component M3 is a block diagonal matrix, showing the
closed-loop effects of circular flow caused by the own exogenous shocks on the
accounts.
The decomposition of SAM multipliers identifies the channels through which
income effects can be produced and transmitted throughout the economy. Logically, this
kind of information is very useful for establishing the origin of income shocks on
economic agents and institutions, and it provides deeper insights into the circular flow
of income.
In order to better interpret the results, we perform an additive decomposition of
the multiplier matrix. This decomposition, proposed by Stone (1978), uses an additive
2
Note that the decomposition in equation (2) is not unique. In consequence, the interpretation of the decomposed
multipliers depends basically on the division of the matrix of expenditure share coefficients, that is, the structure of

matrix A .
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formula calculated by a simple transformation of the previous multiplicative division to
identify each effect:
M  M 3 M 2 M1  I  M1  I   M 2  I  M1  M 3  I  M 2 M1 .
(6)
where I includes the initial injection of income that begins the entire multiplier process,
M1  I  shows the net contribution of own effects in net terms, M 2  I  M1 quantifies
the open net effects and, finally, M 3  I  M 2 M1 represents the net contribution of the
circular effects.3
It should be pointed out that, in addition to this multiplier decomposition
process, some authors have proposed alternative analyses. For example, Defourny and
Thorbecke (1984) proposed the so-called structural or trajectory analysis.4 This method
observes the paths along which the multipliers travel and has the advantage of obtaining
the entire network through which the influence is transmitted, from a source account to
a destination account.
The multiplicative decomposition shown does not enable the results to be
interpreted immediately. Conversely, the additive decomposition proposed by Stone
(1978) allows us to use an additive formula to reveal the contribution made by each
individual effect to the total multiplier effect using an additive formula.
To obtain the additive division, we use the following transformation of the
above expression (6):
M  I  M1  I   M 2  I M1  M 3  I M 2 M1.
(7)
This new expression (7) leads to the total net multiplier effect, that is M  I  ,
which is the result of the aggregation of the own net effects M1  I  , the open net
effects M 2  I  M1 and, lastly, the circular net effects M 3  I  M 2 M1 .
4. Extension of the SAM Model with Greenhouse Emissions: the NAMEA Model
The SAM model can be extended to account for the environmental pollution
associated with production and consumption activities, which are considered
endogenous in the definition of the model. This extension integrates the economic and
ecological relations that take place in environmental pollution and is a useful instrument
of environmental analysis and control.
Let B be the matrix of greenhouse emissions per unit of endogenous income. In
this matrix each element is the amount of gas type i (in physical units) per monetary
unit of endogenous income in account j. That is:
3
There are many examples of this method in the literature. We can cite Bottiroli and Targetti (1988) in the area of
income distribution, Khan (1999) in the analysis of poverty, Xie (2000) for topics related to the environment, and de
Miguel et al. (1998) and Llop and Manresa (1999) in regional studies.
4 For an extended view of this method and possible empirical applications, see Crama, Defourny and Gazón (1984);
Polo, Roland-Host and Sancho (1991b); Sonis, Hewings and Sulistyowati (1997); Thorbecke (1998); Azis (1999);
Ferri and Uriel (2000) and Roberts (2005).
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B  E (Yˆ ) 1 
E
,
Yˆ
(8)
where E is a matrix of total greenhouse emissions made from the endogenous accounts
of the model (i.e. activities of production, factors and consumers), and Yˆ is the diagonal
matrix of the elements in vector Y of endogenous income.
The amount of emissions associated with a given level of exogenous income
(X) can then be calculated as follows:
F  BI  A1 X ,
(9)
where F is the vector of i greenhouse emissions. The elements in matrix B(I − A)−1 are
the emission multipliers, which measure the amount of type i emissions caused by
exogenous and unitary inflows to account j. With this approach we can therefore
analyse how unitary changes in the exogenous demand (an increase or decrease in
investment and exports, for example) affect the amount of greenhouse emissions. This
information is valuable for environmental protection since it shows the environmental
impacts associated with production activities, factors of production and private
consumption.
Taking into account expressions (4), (5), (6) and (7), the NAMEA emission
multiplier matrix can be decomposed into:
F  BM  BI  A  BM 3M 2 M1 .
1
(10)
According to the additive decomposition of the income multipliers
(expressions (6) and (7)), the NAMEA multiplier matrix of polluting emissions can be
divided into:
F  BM  I   BM1  I   BM 2  I M1  BM 3  I M 2 M1.
(11)
This expression allows the total net emission multipliers to be obtained: that is
to say, F  BM  I  , as a result of aggregating the net own effects BM 1  I  , the net
open effects, BM 2  I M1 , and finally, the net circular effects, BM 3  I  M 2 M 1.
The BM 1  I  matrix, or the own effects matrix, captures the effects that a
group of accounts has on itself as a consequence of internal transfers in the own group.
On the other hand, the BM 2  I M1 matrix captures the net open effects of the
multiplier process on the accounts belonging to the other parts of the income flow after
income has been injected in each account.
Finally, the BM 3  I M 2 M1 matrix shows the circular net effects of an injection
of exogenous income that goes through the system and returns to its point of origin.
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5. Empirical Application to Catalan Greenhouse Emissions
Our analysis is based on SAM methodology, which reflects the relationships
between demand and production, production and income, and income and demand. 5 We
also extend these relations to the effects on greenhouse pollution in the regional
economy. The analytical framework developed in Section 4 shows how the exogenous
and unitary inflows to production activities, factors, and consumers affect greenhouse
gas emissions. Therefore, it quantifies how much increase there is in greenhouse
emissions when there is a unit of increase in exogenous demand.
The NAMEA for Catalonia, integrates the SAM database described in the
section above with the Satellite Account on Atmospheric Emissions (IDESCAT, 2008).
Our database is therefore applied to atmospheric emissions and it is constructed by
adding columns of the greenhouse gases emitted by production activities and
consumption.
The information in the account on atmospheric emissions includes the
discharge of pollutants generated by sectors and consumption. This database originally
included the emissions of eleven pollutants. As our aim was to model greenhouse
effects, we used only the three emissions that show greenhouse pollution in the regional
economy. The three gases we analysed are those that must follow the guidelines of the
Kyoto Protocol: carbon dioxide (CO2), methane (CH4), nitrogen monoxide (N2O).
The information provided by the model shows several aspects of greenhouse
pollution. We shall first focus on the emission multipliers that quantify the changes in
the levels of emissions caused by changes in exogenous inflows.
5.1. Emission Multipliers
In the emission multipliers, sectors, production factors, labour (gross wages
and salaries plus social contributions) and capital (the gross operating surplus) were
considered as endogenous items. The private sector, which includes consumers, was
also considered. Finally, the productive branches are disaggregated into the 27 sectors
that appear in our social and environmental accounting matrix.
On the other hand, the savings and investment accounts of the economy (gross
capital formation) were considered as exogenous items. The public sector (public
administration consumption), net production tax, net product tax, indirect taxes
connected with production, import taxes, value added tax and the foreign sector are also
exogenous components of the model.
The emission multipliers show how the production sectors and consumers are
linked to the pollution they generate. In the SAM model, an increase in exogenous
demand leads to an increase in endogenous income. At the same time, the direct
relationship between pollution levels and endogenous income means that an increase in
the latter increases the former.
5
In contrast, the calculation of multipliers in the traditional input-output model omits the relationships in the circular
flow of income from the productive sector towards the primary factors revenue and public or private expenditure. It
also omits the feedback effects from these to the productive sectors. Although the input-output model captures the
impact of changes in final demand on productive sectors, the chain of events is interrupted at this point since it does
not take into account the impact of production on income, consumption and savings.
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In this section we examine how greenhouse gas emissions change in response
to exogenous and unitary changes in the exogenous demand for production activities,
consumption and factors of production, which are endogenous components in model.
We can then identify the agents that cause the highest levels of pollution, which is
valuable information for designing policies to reduce greenhouse gases and satisfy the
Kyoto Protocol.
Table 2 contains the emission multipliers in matrix B(I - A)-1. They show the
changes in Catalan emissions when there is an exogenous and unitary inflow to the
endogenous accounts (production, factors and consumers). Table 2 should be read as
follows: the first row and the first column indicate that when agriculture is subject to an
exogenous and unitary increase in its exogenous demand, CH4 emissions increase by
0.021919 tonnes.
[PLACE TABLE 2 HERE]
The sum of the columns in table 2 shows the increase in emissions for each
greenhouse gas when there is a unitary injection in the exogenous demand for all
accounts simultaneously. These total values, then, reflect the effects on each type of
emission caused by the joint inflows to all sectors of production, factors and consumers.
The pollutant most affected is CH4, which increases by 0.088895 tons per unitary
increase in all the endogenous components of the model. CO2 emissions increase by
0.010082 kilotons and N2O emissions increase by 0.002603 tons.
Table 2 shows which accounts have the greatest influence on greenhouse gas
emissions when they receive exogenous inflows. For example, the first column shows
that one unit of new exogenous demand to sector 1 (agriculture) generates 0.021919
tons of CH4. One unit of new exogenous demand to sector 26 (other services, social
activities, and personal services), on the other hand, generates 0.013565 tons of CH4. In
the second column, the energy sectors (sectors 3 and 4) and sector 11 (other nonmetallic mineral products) generate 0.000726 kilotons of CO2, 0.000663 kilotons of
CO2, and 0.002071 kilotons of CO2, respectively. In the third column, sector 1
(agriculture) generates 0.000865 tons of N2O to meet a new unit of exogenous demand,
while sector 5 (food) generates 0.000224 tons of N2O.
The conclusions we can draw from table 2 are that greenhouse gas emissions in
Catalonia are affected very differently at the sectorial level and that the effects of
production activities, factors and consumption on air pollution are very heterogeneous.
Our results also show that the quantitative increases in greenhouse gas emissions will
essentially depend on the account that receives the exogenous inflow in demand.
5.2. Changes in the Greenhouse Emission Multipliers
In this section we analyse the impact on emission multipliers of a 10%
reduction in total greenhouse gas emissions. This percentage of reduction in emissions
would bring the Catalan economy in line with the total amount of emissions allowed by
the Kyoto Protocol. We considered three scenarios. First we simulated a 10% reduction
in greenhouse gas emissions together with a 5% decrease in endogenous income. Then
we simulated a 10% reduction in emissions with a 10% increase in endogenous income.
Finally, we simulated a 10% reduction in emissions with a 5% increase in endogenous
income.
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The simulation analysis involved modifying the emissions per unit of
endogenous income in matrix B. According to expression (8), in the simulations we
reduced the total emissions used to calculate matrix B (i.e. the E values) by 10%. In the
three situations analysed we varied the endogenous income in the diagonal matrix Yˆ by
different amounts. In the first simulation, we decreased the values of Yˆ by 5%. In the
second and third situations we increased the values of Yˆ by 10% and 5%, respectively.
Table 3 shows the overall impact on emission multipliers of a 10% reduction in
greenhouse gas emissions and a 5% decrease in endogenous income. The last row in
table 3 shows the changes in the emissions of the corresponding gas when there is an
exogenous inflow to all the endogenous accounts of the model. In this situation, there is
a general increase in the emissions of all greenhouse gases. CH4 emissions increase by
12.18%, CO2 emissions by 11.75% and N2O emissions by 12.43%.
[PLACE TABLE 3 HERE]
Table 3 also shows which accounts generate the greatest increases in gas
emissions when they receive an exogenous and unitary inflow. The first column shows
that the highest increases in CH4 are caused by sector 14 (electrical equipment,
electronics and optics) with an increase of 33.09%, and by sector 21 (financial
intermediation), with an increase of 32.47%. The highest increases in CO2 emissions are
caused by sector 27 (homes that employ domestic staff) with an increase of 27.22%, and
sector 21 (financial intermediation) with an increase of 26.11%. The highest increases in
N2O emissions are caused by sector 14 (electrical equipment, electronics and optics)
and sector 21 (financial intermediation), with values of 34.06% and 33.79%,
respectively.
Another important aspect of table 3 is that very few sectors reduce their
emission multipliers. Specifically, sector 1 (agriculture) shows a reduction in CH4
emissions of -3.26% and sector 26 (other services, social activities, personal services)
shows a reduction of -0.41%. For CO2 emissions, sector 11 (other non-metallic mineral
products) shows a reduction of -2.30% and sector 3 (energy products, minerals, coke,
petroleum and fuels) shows a reduction of -1.86%. Finally, N2O emissions, sector 1
(agriculture) shows a value of -3.65%.
[PLACE TABLE 4 HERE]
The second scenario analysed was a 10% reduction in total greenhouse gas
emissions combined with a 10% increase in production and factorial and personal
income. Table 4 shows that the total changes in emission multipliers are negative for all
three greenhouse gases. This means that a reduction in total emissions accompanied by
an increase in production and consumer income would reduce emissions per unit of
income in the regional economy. The last row in table 4 shows that there are similar
reductions in greenhouse gases. Specifically, the reduction in CH4 emission is -34.13%,
the reduction in CO2 emission is -34.12% and the reduction in N2O emission is -34.36%.
Another aspect of table 4 that should be mentioned is that all the accounts
reduce greenhouse gas emissions per unit of exogenous inflow (i. e. all the values in this
table are negative). However, the quantitative impact depends on the account and the
type of gas analysed. The largest reductions in greenhouse gas emission multipliers are
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as follows: for CH4, sector 14 (electrical equipment, electronics and optics) -51.51%;
for CO2, sector 27 (homes that employ domestic staff) -47.58%; for N2O, sector 27
(homes that employ domestic staff) -52.22%.
The third scenario analysed was a 10% reduction in greenhouse gas emissions
together with a 5% increase in endogenous income. Table 5 shows a general reduction
in multipliers, though this was not as large as in the previous scenario. Again, all values
in table 5 are negative but the individual changes are different in quantitative terms. The
highest value is for the effects caused by sector 21 (financial intermediation) on N2O
emissions (-35.76%). The smallest value is for the effects caused by sector 1
(agriculture) on N2O emissions (-15.26%).
[PLACE TABLE 5 HERE]
In summary, a reduction in greenhouse gas emissions together with a reduction
in production and factorial and personal income increases emissions per unit of new
income to sectors, factors and households. On the other hand, a reduction in greenhouse
gas emissions together with an increase in production and personal and factorial income
considerably reduces the unitary emissions of all three greenhouse gases in the regional
economy.
The results of these simulations can help policymakers to understand the
consequences of different modifications on economic and ecological relations. This is
essential for the success of environmental policy interventions aimed at ensuring the
quality of the environment and the preservation of natural ecosystems.
5.3. Decomposition of the emission multipliers matrix
Decomposing emission multipliers serves as an interesting exercise, which can
show the relevance of the various interdependent channels of income in the Catalan
economy and their connection with the environment.
Tables 6, 7 and 8 summarize the results of the decomposition analysis, which
consists of calculating the matrices of the own net effects BM 1  I  , the open net
effects BM 2  I M1 and, finally, the circular net effects BM 3  I M 2 M1 . Additionally, the
above mentioned tables reflect the percentages that every net effect contributes to the
total emission multipliers.
Analysis of table 6 reveals that the circular effects (60.25 % of the total effect)
and own effects (31.09 %) have greater weight than the open effects (8.66 %).
On the other hand, in three accounts (labour, capital and households) the open
effects are considerably greater than the circular effects. Nevertheless, in these accounts
and account 27 (homes that employ domestic staff) there are no own effects. The reason
for this is the major interrelationship between the productive sectors. It might also be
consequence of the structure of the NAMEA, since it only presents an account for the
consumption sector, and this can be a limitation when showing the interrelationships
within the private sector of the economy.
[PLACE TABLE 6 HERE]
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Table 7 shows the importance that every effect of interdependence has on the
CO2 emission multiplier. In this table, the circular effects have the greatest weight
(42.41 % of the net total effect). The own effects (33.48 %) are in second place and the
open effects (24.12 %) have least weight.
On the other hand, in both the labour and capital accounts the open effects are
considerably higher than the circular effects and in the consumption account the circular
effects are slightly higher than the open effects. In addition, the own effects of labour,
capital and households and account 27 (homes that employ domestic staff) are void.
[PLACE TABLE 7 HERE]
The information in table 8 shows the importance that every effect of
interdependence has on the total emission multiplier of N20 in every account. In this
table, the circular effects present in general a few top effects that the rest of effects that
we analyze (in twenty-two accounts these effects exceed 60%). The own effects and the
open effects have smaller contributions to total multipliers.
Finally, in the last three accounts (labour, capital and households) the open
effects are considerably higher than the circular effects. The explanation for this is the
important interrelationship between the productive sectors, which means that the
circular effects in these accounts are higher. Another explanation might be the structure
of our social and environmental database, which has a single account for the
consumption sector and, therefore, does not capture the interrelationships within the
private sector of the economy.
[PLACE TABLE 8 HERE]
6. Conclusions
In recent years, natural levels of greenhouse gases have increased due to
emissions of CO2 from fossil fuels, methane, nitrogen oxide produced by agriculture,
changes in soil use, and various inert industrial gases that do not occur naturally. If the
concentration of greenhouse gases continues to increase, the greenhouse effect will
cause a global increase in air temperature that may lead to serious environmental
problems such as climate change, damage to natural ecosystems, and impoverishment of
the environment. All of these negative impacts on the environment will also have
adverse effects on human health.
In this paper we have defined a linear model of emission multipliers for the
Catalan economy in 2001. This model shows how unitary increases in exogenous
demand affect greenhouse gas emissions. The linear SAM model is similar to the inputoutput model designed by Leontief, but the SAM model incorporates a greater level of
endogeneity of the accounts. As a result, it captures the complete circular flow of
income as it is not limited to the production sphere but incorporates income distribution
and the income generation processes.
With the aim of reducing greenhouse gas emissions in the Catalan economy,
we analysed three alternative scenarios. The first scenario was a 10% reduction in
greenhouse gas emissions and a 5% cut in endogenous income (production, and
factorial and private income). This led to an overall increase in emission multipliers of
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all greenhouse gases. Under this scenario, therefore, the Catalan economy would fail to
comply with the objectives of the Kyoto Protocol. The second scenario was a 10%
reduction in greenhouse gas emissions and a 10% increase in endogenous income. This
led to a considerable reduction in the emissions of all greenhouse gases analysed. The
third scenario was a 10% reduction in greenhouse gas emissions and a 5% increase in
endogenous income. This scenario also led to a general reduction in greenhouse gas
emissions per unit of exogenous demand. A decrease in total emissions combined with
an increase in the income of the endogenous accounts would have positive effects on the
environment that would enable the Catalan economy to satisfy the objectives of the
Kyoto Protocol.
Additionally, we also decomposed the total emission multipliers into own
effects, open effects and circular effects. This decomposition shows the different
channels of income generation and its effects on greenhouse gas emissions. For all the
gases considered, the circular effects are the most important component in total
multipliers.
We should bear in mind that policies designed to reduce emissions may
conflict with a society’s development goals since there is a close relationship between
economic growth, the consumption of natural resources, and the generation of pollution
and environmental loads. Policymakers must, therefore, harmonise economic and
ecological objectives in order to ensure both the development of society and the
preservation of the environment.
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Xie, J. (2000) An Environmentally Extended Social
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Table 1. Endogenous and exogenous accounts in a SAM
INCOME
EXPENDITURE
Endogenous
Sum
Exogenous
Sum
Total
Endogenous
Tnn
n
Injections
Tnx
x
Y
Exogenous
Outlays
Txn
l
Residual Balance
Txx
t
X
TOTAL
Y
Z
Source: Defourny and Thorbecke (1984)
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Table 2. Emission multipliers (B(I - A)-1)
CH4
(t)
CO2
(kt)
N2O
(t)
1. Agriculture
0.021919
0.000222
0.000865
2. Fishing
0.000590
0.000207
0.000021
3. Energy, Minerals, Coke, petroleum and fuels
0.000757
0.000726
0.000025
4. Electrical energy, gas and water
0.005290
0.000663
0.000038
5. Food
0.005878
0.000205
0.000224
6. Textile
0.001742
0.000190
0.000056
7. Manufacture of wood and cork
0.002932
0.000178
0.000106
8. Paper
0.001314
0.000203
0.000038
9. Chemistry
0.001107
0.000298
0.000041
10. Rubber and plastic products
0.001240
0.000200
0.000037
11. Other non-metallic mineral products
0.001293
0.002071
0.000061
12. Metal
0.000921
0.000162
0.000025
13. Machinery
0.001146
0.000122
0.000025
14. Electrical equipment, electronics and optics
0.000753
0.000123
0.000021
15. Automobiles
0.000921
0.000147
0.000026
16. Other industries
0.001295
0.000199
0.000037
17. Construction
0.001835
0.000459
0.000054
18. Commerce
0.001945
0.000291
0.000057
19. Hotel management
0.003016
0.000254
0.000096
20. Transport and communications
0.001593
0.000625
0.000100
21. Financial intermediation
0.001644
0.000219
0.000046
22. Real estate activities, entrepreneurial services
0.001695
0.000236
0.000047
23. Public services
0.001882
0.000250
0.000052
24. Education
0.001953
0.000249
0.000054
0.001931
0.000252
0.000080
26. Other services, social and personal services
0.013565
0.000272
0.000127
27. Homes that employ domestic staff
0.001881
0.000229
0.000052
Labour
0.002286
0.000278
0.000064
Capital
0.002286
0.000278
0.000064
Households
0.002286
0.000278
0.000064
Total
0.088895
0.010082
0.002603
25. Sanitary, veterinary activities, social services
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Table 3. Changes (%) in emission multipliers: 10% reduction in emissions and 5%
reduction in endogenous income
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CH4
(t)
CO2
(kt)
N2O
(t)
1. Agriculture
-3.23%
13.25%
-3.65%
2. Fishing
30.29%
6.90%
24.74%
3. Energy, minerals, coke, petroleum and fuels
12.15%
-1.86%
10.35%
4. Electrical energy, gas and water
2.46%
3.45%
25.17%
5. Food
6.62%
18.53%
5.29%
6. Textile
21.93%
20.85%
20.21%
7. Manufacture of wood and cork
12.00%
19.02%
10.00%
8. Paper
28.47%
19.48%
28.69%
9. Chemistry
26.60%
9.16%
20.24%
10. Rubber and plastic products
27.94%
18.71%
27.67%
11. Other non-metallic mineral products
27.25%
-2.30%
15.07%
12. Metal
30.80%
18.37%
32.52%
13. Machinery
23.27%
23.67%
31.09%
14. Electrical equipment, electronics and optics
33.09%
22.40%
34.06%
15. Automobiles
32.29%
22.49%
33.41%
16. Other industries
28.01%
19.33%
28.91%
17. Construction
31.11%
14.19%
31.25%
18. Commerce
30.62%
22.32%
30.96%
19. Hotel management
20.30%
24.08%
19.21%
20. Transport and communications
30.86%
6.44%
12.18%
21. Financial intermediation
32.47%
26.11%
33.79%
22. Real estate activities and entrepreneurial services
31.63%
24.69%
33.34%
23. Public services
28.82%
23.23%
30.77%
24. Education
31.07%
25.61%
32.92%
25. Sanitary, veterinary activities, social services
30.07%
24.56%
19.90%
26. Other services, social and personal services
-0.41%
20.83%
9.63%
27. Homes that employ domestic staff
31.64%
27.22%
33.37%
Labour
25.06%
20.86%
26.70%
Capital
25.06%
20.86%
26.70%
Households
18.80%
14.81%
20.36%
Total
12.18%
11.75%
12.43%
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Table 4. Changes (%) in emission multipliers: 10% reduction in emissions and 10%
increase in endogenous income
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CH4
(t)
CO2
(kt)
N2O
(t)
1. Agriculture
-20.18%
-35.20%
-19.79%
2. Fishing
-49.72%
-29.74%
-44.85%
3. Energy, minerals, coke, petroleum and fuels
-33.63%
-21.97%
-31.94%
4. Electrical energy, gas and water
-25.16%
-27.14%
-44.89%
5. Food
-31.38%
-40.35%
-30.34%
6. Textile
-43.33%
-42.15%
-41.97%
7. Manufacture of wood and cork
-35.72%
-40.49%
-34.17%
8. Paper
-48.07%
-40.90%
-48.04%
9. Chemistry
-46.63%
-32.02%
-41.00%
10. Rubber and plastic products
-47.81%
-40.64%
-47.48%
11. Other non-metallic mineral products
-47.11%
-21.26%
-36.17%
12. Metal
-49.90%
-39.99%
-50.91%
13. Machinery
-44.05%
-44.59%
-49.87%
14. Electrical equipment, electronics and optics
-51.51%
-43.65%
-52.02%
15. Automobiles
-50.79%
-43.68%
-51.42%
16. Other industries
-47.91%
-41.02%
-48.53%
17. Construction
-50.14%
-37.48%
-50.06%
18. Commerce
-49.82%
-43.67%
-49.92%
19. Hotel management
-42.56%
-45.06%
-41.79%
20. Transport and communications
-49.79%
-29.47%
-33.71%
21. Financial intermediation
-51.39%
-46.64%
-52.21%
22. Real estate and entrepreneurial services
-50.64%
-45.55%
-51.76%
23. Public services
-48.58%
-44.36%
-49.91%
24. Education
-50.53%
-46.23%
-51.77%
25. Sanitary, veterinary and social services
-49.63%
-45.42%
-40.42%
26. Other services, social and personal services
-22.59%
-42.09%
-31.43%
27. Homes that employ domestic staff
-51.08%
-47.58%
-52.22%
Labour
-46.19%
-42.34%
-47.45%
Capital
-46.19%
-42.34%
-47.45%
Households
-40.81%
-42.19%
Total
-34.13%
-36.57%
34.12%
-34.36%
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Table 5. Changes (%) in emission multipliers: 10% reduction in emissions and 5%
increase in endogenous income
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CH4
(t)
CO2
(kt)
N2O
(t)
1. Agriculture
-15.51%
-24.85%
-15.26%
2. Fishing
-34.08%
-21.40%
-31.01%
3. Energy, minerals, coke, petroleum and fuels
-23.97%
-16.52%
-22.93%
4. Electrical energy, gas and water
-18.64%
-19.68%
-31.09%
5. Food
-22.06%
-28.00%
-21.38%
6. Textile
-29.87%
-29.18%
-28.99%
7. Manufacture of wood and cork
-24.87%
-28.14%
-23.86%
8. Paper
-33.05%
-28.40%
-33.06%
9. Chemistry
-32.10%
-22.78%
-28.57%
10. Rubber and plastic products
-32.84%
-28.16%
-32.65%
11. Other non-metallic mineral products
-32.42%
-16.13%
-25.58%
12. Metal
-34.24%
-27.81%
-34.96%
13. Machinery
-30.41%
-30.72%
-34.27%
14. Electrical equipment, electronics and optics
-35.33%
-30.10%
-35.70%
15. Manufacture of transport material
-34.87%
-30.12%
-35.33%
16. Other industries
-32.90%
-28.43%
-33.32%
17. Construction
-34.40%
-26.01%
-34.38%
18. Commerce
-34.18%
-30.09%
-34.27%
19. Hotel management
-29.27%
-30.99%
-28.75%
20. Transport and communications
-34.20%
-21.20%
-24.02%
21. Financial intermediation
-35.18%
-32.03%
-35.76%
22. Real estate and entrepreneurial services
-34.71%
-31.31%
-35.48%
23. Public services
-33.33%
-30.55%
-34.24%
24. Education
-34.57%
-31.76%
-35.43%
25. Sanitary, veterinary and social services
-34.01%
-31.23%
-28.26%
26. Other services, social and personal services
-17.04%
-29.15%
-22.59%
27. Homes that employ domestic staff
-34.91%
-32.62%
-35.70%
Labour
-31.66%
-29.26%
-32.49%
Capital
-31.66%
-29.26%
-32.49%
Households
-28.24%
-25.72%
-29.11%
Total
-24.20%
-24.13%
-24.35%
Albacete-23-25 de sept. 2009
Analysis of accounting multipliers in a NAMEA:
An application to the case of Catalonia
23
Table 6. Additive decomposition in the emissions of CH4 (t)
Own Effects
Open Effects
Circular Effects
Total
Effects
Value
(%)
Value
(%)
Value
(%)
1. Agriculture
0.001247
58.84%
0.000014
0.68%
0.000858
40.48%
0.002120
2. Fishing
0.000101
17.31%
0.000008
1.36%
0.000477
81.33%
0.000586
3. Energy, minerals, coke, petroleum and fuels
0.000153
36.67%
0.000004
1.04%
0.000260
62.29%
0.000418
4. Electrical energy, gas and water
0.000494
35.54%
0.000015
1.06%
0.000882
63.39%
0.001391
5. Food
0.005008
85.24%
0.000014
0.24%
0.000853
14.52%
0.005875
6. Textile
0.000819
47.07%
0.000015
0.87%
0.000905
52.06%
0.001739
7. Manufacture of wood and cork
0.002107
71.97%
0.000014
0.46%
0.000807
27.57%
0.002928
8. Paper
0.000354
27.01%
0.000016
1.20%
0.000941
71.78%
0.001310
9. Chemistry
0.000313
29.78%
0.000012
1.16%
0.000725
69.06%
0.001050
10. Rubber and plastic products
0.000357
28.86%
0.000015
1.17%
0.000866
69.97%
0.001238
11. Other non-metallic mineral products
0.000303
24.17%
0.000016
1.25%
0.000935
74.58%
0.001253
12. Metal
0.000173
18.86%
0.000012
1.34%
0.000733
79.80%
0.000918
13. Machinery
0.000439
38.33%
0.000012
1.02%
0.000695
60.65%
0.001146
0.000118
15.71%
0.000010
1.39%
0.000624
82.90%
0.000753
14. Electrical equipment, electronics and optics
15. Manufacture of transport material
0.000190
20.68%
0.000012
1.31%
0.000718
78.01%
0.000920
16. Other industries
0.000409
31.67%
0.000015
1.13%
0.000869
67.21%
0.001293
17. Construction
0.000330
18.01%
0.000025
1.35%
0.001479
80.63%
0.001835
18. Commerce
0.000304
15.65%
0.000027
1.39%
0.001613
82.96%
0.001944
19. Hotel management
0.001429
47.38%
0.000026
0.87%
0.001560
51.75%
0.003015
20. Transport and communications
0.000228
14.74%
0.000022
1.41%
0.001298
83.85%
0.001548
21. Financial intermediation
0.000091
5.54%
0.000026
1.56%
0.001527
92.90%
0.001644
22. Real estate and entrepreneurial services
0.000191
11.25%
0.000025
1.46%
0.001479
87.29%
0.001695
23. Public services
0.000314
16.68%
0.000026
1.37%
0.001542
81.95%
0.001881
24. Education
0.000100
5.12%
0.000031
1.57%
0.001822
93.32%
0.001952
25. Sanitary, veterinary and social services
0.000259
13.40%
0.000028
1.43%
0.001644
85.17%
0.001930
0.000737
33.30%
0.000024
1.10%
0.001452
65.60%
0.002214
27. Homes that employ domestic staff
0.000000
0.00%
0.000031
1.65%
0.001850
98.35%
0.001881
Labour
0.000000
0.00%
0.001387
60.67%
0.000899
39.33%
0.002286
Capital
0.000000
0.00%
0.001387
60.67%
0.000899
39.33%
0.002286
Households
0.000000
0.00%
0.001349
60.01%
0.000899
39.99%
0.002248
Total
0.016570
31.09%
0.004616
8.66%
0.032111
60.25%
0.053297
26. Other services, social and personal services
JEIOAB09
Albacete-23-25 de sept. 2009
24
Llop, M; Pié, L
Table 7. Additive decomposition in the emissions of CO2 (kt)
Own Effects
Open Effects
Circular Effects
Total
Effects
Value
(%)
Value
(%)
Value
(%)
1. Agriculture
0.000033
23.81%
0.000034
24.55%
0.000072
51.64%
0.000139
2. Fishing
0.000045
43.30%
0.000019
18.27%
0.000040
38.43%
0.000104
3. Energy, minerals, coke, petroleum and fuels
0.000160
83.25%
0.000010
5.40%
0.000022
11.35%
0.000192
4. Electrical energy, gas and water
0.000227
67.57%
0.000035
10.45%
0.000074
21.98%
0.000336
5. Food
0.000078
42.68%
0.000034
18.47%
0.000071
38.85%
0.000184
6. Textile
0.000056
33.41%
0.000036
21.46%
0.000076
45.13%
0.000168
7. Manufacture of wood and cork
0.000050
33.40%
0.000032
21.46%
0.000068
45.14%
0.000150
8. Paper
0.000055
32.32%
0.000037
21.81%
0.000079
45.87%
0.000172
9. Chemistry
0.000095
51.38%
0.000029
15.67%
0.000061
32.95%
0.000184
10. Rubber and plastic products
0.000074
40.91%
0.000034
19.04%
0.000073
40.05%
0.000181
11. Other non-metallic mineral products
0.000275
70.42%
0.000037
9.53%
0.000078
20.05%
0.000390
12. Metal
0.000044
32.52%
0.000029
21.74%
0.000061
45.74%
0.000134
13. Machinery
0.000030
26.02%
0.000028
23.84%
0.000058
50.14%
0.000116
14. Electrical equipment, electronics and optics
0.000041
34.64%
0.000025
21.06%
0.000052
44.30%
0.000118
15. Manufacture of transport material
0.000051
36.43%
0.000029
20.48%
0.000060
43.09%
0.000139
16. Other industries
0.000072
40.13%
0.000035
19.29%
0.000073
40.58%
0.000179
17. Construction
0.000271
59.71%
0.000059
12.98%
0.000124
27.31%
0.000454
18. Commerce
0.000083
29.48%
0.000064
22.72%
0.000135
47.80%
0.000283
19. Hotel management
0.000055
22.16%
0.000062
25.08%
0.000131
52.76%
0.000248
20. Transport and communications
0.000164
50.49%
0.000052
15.95%
0.000109
33.56%
0.000324
21. Financial intermediation
0.000027
12.67%
0.000061
28.14%
0.000128
59.19%
0.000216
22. Real estate and entrepreneurial services
0.000048
20.88%
0.000059
25.49%
0.000124
53.63%
0.000231
23. Public services
0.000051
21.10%
0.000061
25.42%
0.000129
53.48%
0.000241
24. Education
0.000018
7.36%
0.000073
29.85%
0.000153
62.79%
0.000243
25. Sanitary, veterinary and social services
0.000043
17.36%
0.000065
26.63%
0.000138
56.01%
0.000246
26. Other services, social and personal services
0.000057
24.13%
0.000058
24.45%
0.000122
51.42%
0.000236
27. Homes that employ domestic staff
0.000000
0.00%
0.000074
32.22%
0.000155
67.78%
0.000229
Labour
0.000000
0.00%
0.000168
60.67%
0.000109
39.33%
0.000278
Capital
0.000000
0.00%
0.000168
60.67%
0.000109
39.33%
0.000278
Households
0.000000
0.00%
0.000079
41.97%
0.000109
58.03%
0.000188
Total
0.002203
33.48%
0.001587
24.12%
0.002791
42.41%
0.006580
JEIOAB09
Albacete-23-25 de sept. 2009
Analysis of accounting multipliers in a NAMEA:
An application to the case of Catalonia
25
Table 8. Additive decomposition in the emissions of N2O (t)
Own Effects
Open Effects
Circular Effects
Total
Effects
Value
(%)
Value
(%)
Value
(%)
1. Agriculture
0.000048
66.38%
0.000000
0.39%
0.000024
33.23%
0.000072
2. Fishing
0.000005
25.57%
0.000000
0.86%
0.000013
73.57%
0.000018
3. Energy, minerals, coke, petroleum and fuels
0.000005
39.20%
0.000000
0.70%
0.000007
60.10%
0.000012
4. Electrical energy, gas and water
0.000008
24.26%
0.000000
0.87%
0.000025
74.87%
0.000033
5. Food
0.000199
89.19%
0.000000
0.12%
0.000024
10.69%
0.000223
6. Textile
0.000030
54.06%
0.000000
0.53%
0.000025
45.41%
0.000056
7. Manufacture of wood and cork
0.000083
78.39%
0.000000
0.25%
0.000023
21.37%
0.000106
8. Paper
0.000011
29.10%
0.000000
0.82%
0.000026
70.09%
0.000038
9. Chemistry
0.000012
36.15%
0.000000
0.74%
0.000020
63.12%
0.000032
10. Rubber and plastic products
0.000012
32.88%
0.000000
0.77%
0.000024
66.35%
0.000037
11. Other non-metallic mineral products
0.000010
27.67%
0.000000
0.83%
0.000026
71.49%
0.000037
12. Metal
0.000004
16.29%
0.000000
0.96%
0.000021
82.75%
0.000025
13. Machinery
0.000005
21.14%
0.000000
0.91%
0.000019
77.95%
0.000025
14. Electrical equipment, electronics and optics
0.000004
17.17%
0.000000
0.95%
0.000017
81.88%
0.000021
15. Manufacture of transport material
0.000006
21.59%
0.000000
0.90%
0.000020
77.51%
0.000026
16. Other industries
0.000012
32.97%
0.000000
0.77%
0.000024
66.26%
0.000037
17. Construction
0.000012
21.71%
0.000000
0.90%
0.000041
77.39%
0.000053
18. Commerce
0.000011
19.10%
0.000001
0.93%
0.000045
79.97%
0.000056
19. Hotel management
0.000052
54.04%
0.000001
0.53%
0.000044
45.43%
0.000096
20. Transport and communications
0.000018
33.31%
0.000000
0.77%
0.000036
65.92%
0.000055
21. Financial intermediation
0.000003
6.68%
0.000000
1.08%
0.000043
92.25%
0.000046
22. Real estate and entrepreneurial services
0.000005
11.15%
0.000000
1.02%
0.000041
87.83%
0.000047
23. Public services
0.000008
15.49%
0.000001
0.97%
0.000043
83.53%
0.000052
24. Education
0.000002
4.55%
0.000001
1.10%
0.000051
94.35%
0.000054
25. Sanitary, veterinary and social services
0.000009
16.51%
0.000001
0.96%
0.000046
82.53%
0.000056
26. Other services, social and personal services
0.000014
25.10%
0.000000
0.86%
0.000041
74.04%
0.000055
27. Homes that employ domestic staff
0.000000
0.00%
0.000001
1.15%
0.000052
98.85%
0.000052
Labour
0.000000
0.00%
0.000039
60.67%
0.000025
39.33%
0.000064
Capital
0.000000
0.00%
0.000039
60.67%
0.000025
39.33%
0.000064
Households
0.000000
0.00%
0.000038
60.21%
0.000025
39.79%
0.000063
Total
0.000587
36.48%
0.000125
7.74%
0.000898
55.78%
0.001610
JEIOAB09
Albacete-23-25 de sept. 2009
26
JEIOAB09
Llop, M; Pié, L
Albacete-23-25 de sept. 2009